https://ej-eng.org/index.php/ejeng/issue/feed European Journal of Engineering and Technology Research 2022-07-07T03:07:49-04:00 Editor-in-Chief editor@ej-eng.org Open Journal Systems European Journal of Engineering and Technology Research https://ej-eng.org/index.php/ejeng/article/view/2840 Managing Energy demand in Smart Houses Using Sensors and Logic Circuits 2022-06-13T18:27:55-04:00 Ahmed M. D. E. Hassanein ahmed.diaa.hassanein@gmail.com <p>There is an ever increasing demand on consumption of electrical energy but the production of energy is limited. A growing need to design smart houses that would reduce consumption of electrical energy is emerging. In this paper we propose a simple electrical design based on logic circuits and sensors. The paper also addresses one of the main concerns of third world countries in which a country needs to encourage dependence on local designs and products and reduce importing goods using foreign currency. The sensors detect daytime and presence of people inside houses. Based on these two pieces of information, we can make our houses tailored to put light on and off just to serve the needs of the users without any losses. The electrical design is very simple and saves almost 15% of the consumed energy.</p> 2022-07-28T00:00:00-04:00 Copyright (c) 2022 Ahmed M. D. E. Hassanein https://ej-eng.org/index.php/ejeng/article/view/2855 An Effective Short-Term Electrical Load Forecasting Model: A Constructive Neural Network Approach 2022-07-03T11:17:52-04:00 Kazi Rafiqul Islam rafiqul@duet.ac.bd Md. Monirul Kabir munir@duet.ac.bd Amit Shaha Surja amit@sec.ac.bd Md. Shahid Iqbal iqbal@sec.ac.bd <p class="p1">In this paper, an Effective Electrical Load Forecasting (EELF) model has been introduced based on Feed-Forward Neural Network (FFNN) which utilizes the constructive method during training. The key aspect of this model is to automate the FFNN architecture during training phase in order to forecast the electrical load. Thus, the robustness of standard FFNN increases while forecasting the electrical load. Moreover, this proposed model can efficiently overcome the existing limitations of FFNN to successfully predict the fast load changes and also the holiday loads. The model has been named as Constructive Approach for Effective Electrical Load Forecasting (CAEELF) on a short-term basis. In order to evaluate the performance of CAEELF, Spain's daily electrical load demand data have been used. Furthermore, extensive experimental results and comparisons have been shown to validate the acceptability of proposed CAEELF for electrical load prediction over other standard FFNN models.</p> 2022-08-02T00:00:00-04:00 Copyright (c) 2022 Kazi Rafiqul Islam, Md. Monirul Kabir, Amit Shaha Surja, Md. Shahid Iqbal https://ej-eng.org/index.php/ejeng/article/view/2841 Relationship between Different Anaerobic Digestion Parameters in a Pig-dung Aided Water Hyacinth Digestion Process 2022-06-15T05:50:45-04:00 Ochuko M. Ojo omojo@futa.edu.ng Josiah O. Babatola jobabatola@futa.edu.ng Taiwo O. Olabanji oreoluwataiwo27@gmail.com <p>This study is aimed at assessing the relationship between different anaerobic digestion parameters (biogas quality, retention time, pH, temperature, biogas pressure, volume of biogas produced and cumulative volume of gas produced) in a Pig-dung (PD) aided Water Hyacinth (WH) digestion process in order to maximize biogas yield in terms of quantity and quality. 25 - litre capacity plastic prototype digesters were used in the study and eleven (11) mix ratios of PD and WH were prepared namely: 10 WH: 0 PD, 9 WH: 2 PD, 8 WH: 2 PD, 7 WH: 3 PD, 6 WH: 4 PD, 5 WH: 5 PD, 4 WH: 6 PD, 3 WH: 7 PD, 2 WH: 8 PD, 1 WH: 9 PD and 0 WH: 10 PD. The digestion process was evaluated for a retention period of 40 days. A bivariate Pearson correlation analysis was carried out to examine the relationship between the quality of gas produced and other variables. The results revealed that daily gas production yields greatly improved in the co-digestion runs with mix 3 WH: 7 PD recording the highest maximum daily yield of 9.5 L with a cumulative gas volume of 140 L. For this mix, the methane content of the gas produced increased from 5.8% on day 4 to 69.2% on day 20. The least quantity and quality of gas was produced by mix 10 WH: 0 PD with a maximum daily yield 2.34 L and a cumulative gas yield of 32.18 L. The digestion of all the mixes occurred within a mesophilic temperature range of 28.2 to 31.4 0C and an increase in temperature within the digestion resulted in an increase in the quality of gas produced. The gas pressure ranged from 1 bar to 3.324 bars with an increase in gas pressure leading to a corresponding increase in volume of gas produced. The pH of the substrates ranged from 6.1 to 8.4 with the values low at the start of the digestion process and gradually increasing to its maximum at the end of the digestion process. The results revealed a very strong, positive and significant association between the quality of the biogas produced and other digestion parameters.</p> 2022-08-01T00:00:00-04:00 Copyright (c) 2022 Ochuko M. Ojo, Josiah O. Babatola, Taiwo O. Olabanji https://ej-eng.org/index.php/ejeng/article/view/2861 Performance Analysis of Different Convolutional Neural Network (CNN) Models with Optimizers in Detecting Tuberculosis (TB) from Various Chest X-ray Images 2022-07-07T03:07:49-04:00 Salman F. Rabby salman.eee.1450@gmail.com Anamul Hasan anamul-hasan@outlook.com Md. Janibul A. Soeb janibul.fpm@sau.ac.bd Gourob P. Shirsho shirshopathok@gmail.com Bijoy Talukdar talukderbijoy@gmail.com <p class="p1">Tuberculosis (TB) is one of the top 10 infectious disease-related deaths. This paper uses Convolutional Neural Networks (CNN) to investigate the accuracy and performance of three pre-trained models with different optimizers and loss functions to diagnose tuberculosis based on the patient's chest X-ray scans. The odds of treating and curing tuberculosis (TB) are better if the disease is diagnosed early in a patient. Early detection of tuberculosis could lead to a decreased overall mortality rate. The best and quickest way to identify tuberculosis is to look at the patient's chest X-Ray image (CXR). A qualified professional Radiologist is required to make an accurate diagnosis. But do not have qualified doctor or radiologist everywhere. On the other hand, it is quite difficult for a doctor or radiologist to diagnose from any x-ray images with open eyes. 914 normal chest x-ray images and 892 TB infected images were used from different sources to train and evaluate these images to detect the exact x-ray of Tuberculosis infected people. Different famous pre-trained models like VGG16, InceptionV3 and Xception etc. were applied. Approximately 80% of the data was used for training and the remaining 20% was used for validation. From all of these datasets, randomly 190 images from normal and 180 images from TB chest x-ray images have been taken. Those randomized 370 (190 for TB and 180 for normal) images were used to evaluate the data finally. Performance of different algorithm like VGG16, InceptionV3 and Xception by applying different optimizers (Adam, Adadelta, Adagrad, Adamax, RMSprop, Nadam, SGD), different loss functions (Binary Cross Entropy, Hinge, Squared Hinge), varying input image size and also varying batch size were also been recorded. Note that, huge variations of performance for different combinations of algorithm, optimizer, loss function, input image size, batch size have been observed. Confusion matrix, precision, recall, f1-score value have also been recorded to understand and justify how accurately the model is predicting the disease from different angles.</p> 2022-08-04T00:00:00-04:00 Copyright (c) 2022 Salman F. Rabby, Anamul Hasan, Md. Janibul A. Soeb, Gourob P. Shirsho, Bijoy Talukdar https://ej-eng.org/index.php/ejeng/article/view/2850 Development of the Moisturizing Cleansing Pad Having Sterilizing and Anti-bacterial Effect against Covid-19 2022-06-24T07:22:49-04:00 In-Young Kim iykim200@naver.com Ji-Min Noh biobeautech@naver.com Eun-Kyung Seul biobeautech@naver.com Joo-Youb Lee jake20@jwu.ac.kr <p class="p1">This study is to study the development of a moisturized cleansing pad with sterilization and antibacterial effects in respond to COVID-19. Using MimicLipid MSM-1000 of the nonionic surfactant was developed with the O/W emulsion micelles that are forming multilamellar structures. Chlorine dioxide and chlorhexidine digluconate as antibacterial sanitizer were encapsulated and stabilized in these micelles, so that it could be remained stable even after long-term use. The appearance of formula-1 is a milky white liquid, and it has a characteristic odor. The pH of this formulation was 6.1. Also, the specific gravity is 1.014 and it has soft and moist texture. Moreover, there is no stinging or irritation when applied to the skin. The particle size distribution of this emulsion was about 1-3 ?m. As a result of quantitative analysis of chlorine dioxide which is enclosed in O/W emulsion after 8 weeks, it was found that 85.59% or more at 45 ÅãC and 93.47% at 25 ÅãC. The skin moisturizing effect of O/W-emulsion increased to 58.9% immediately after the sample was applied, 28.5% after 4 hours and 17.3% after 8 hours, showing 3.3 times better effect than before application, and about 5.4 times better moisturizing power than the control group. The cleansing power of the O/W emulsion was not very high in the case of a simple 60% ethanol solution for makeup residues, lipstick, and fine dust. On the other hand, in Formula-1, all three were found to have excellent cleansing power. As an application product of the cosmetics industry, a pouch-type moisturizing cleansing pad was developed and commercialized, and a product having a sterilization effect and an anti-bacterial effect could be developed without using ethanol.</p> 2022-08-10T00:00:00-04:00 Copyright (c) 2022 In-Young Kim, Ji-Min Noh, Eun-Kyung Seul, Joo-Youb Lee